ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal最新文献_第5页

Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning Technique 基于深度学习的异步电动机性能分析与故障检测

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-01-24 DOI: 10.14201/adcaij.28435

Pradeep Katta, Karunanithi Kandasamy, Raja Soosaimarian Peter Raj, R. Subramanian, C. Perumal

{"title":"Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning Technique","authors":"Pradeep Katta, Karunanithi Kandasamy, Raja Soosaimarian Peter Raj, R. Subramanian, C. Perumal","doi":"10.14201/adcaij.28435","DOIUrl":"https://doi.org/10.14201/adcaij.28435","url":null,"abstract":"The recent improvements related to the area of electric locomotive, power electronics, assembly processes and manufacturing of machines have increased the robustness and reliability of induction motors. Regardless of the increased availability, the application of induction motors in many fields alleges the need for operating state supervision and condition monitoring. In other words, fault identification at the initial stage helps make appropriate control decisions, influencing product quality as well as providing safety. Inspired by these demands, this work proposes a regression based modeling for the analysis of performance in induction motors. In this approach, the feature extraction process is combined with classification for efficient fault detection. Deep Belief Network (DBN) stacked with multiple Restricted Boltzmann Machine (RBM) is exploited for the robust diagnosis of faults with the adoption of training process. The influences of harmonics over induction motors are identified and the losses are mitigated. The simulation of the suggested approach and its comparison with traditional approaches are executed. An overall accuracy of 99.5% is obtained which in turn proves the efficiency of DBN in detecting faults.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"9 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84408251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The question of "Mind-sets" and AI: Cultural origins and limits of the current AI Ethical AIs and Cultural Pluralism “思维模式”与人工智能的问题:当前人工智能的文化起源与局限、伦理人工智能与文化多元主义

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-01-04 DOI: 10.30564/aia.v4i2.5156

Badrudin Amershi

引用次数: 0

Distributed Artificial Intelligence: 4th International Conference, DAI 2022, Tianjin, China, December 15–17, 2022, Proceedings 分布式人工智能:第四届国际会议，DAI 2022，天津，中国，12月15-17日，2022，会议录

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-25549-6

引用次数: 0

Optimization of Window Size for Calculating Semantic Coherence Within an Essay 计算文章语义连贯的窗口大小优化

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-10-21 DOI: 10.14201/adcaij.27184

Kshitiz Srivastava, Namrata Dhanda, Anurag Shrivastava

引用次数: 1

An Ensemble Classification and Regression Neural Network for Evaluating Role-based Tasks Associated with Organizational Unit 基于集成分类和回归神经网络的组织单元角色任务评价

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-10-21 DOI: 10.14201/adcaij.26764

M. Abbod, A. Alrashedi

引用次数: 0

CHOP: Maximum Coverage Optimization and Resolve Hole Healing Problem using Sleep and Wake-up Technique for WSN CHOP:基于睡眠和唤醒技术的WSN最大覆盖优化和洞愈合问题

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-10-21 DOI: 10.14201/adcaij.27271

Vipul Narayan, A. Daniel

{"title":"CHOP: Maximum Coverage Optimization and Resolve Hole Healing Problem using Sleep and Wake-up Technique for WSN","authors":"Vipul Narayan, A. Daniel","doi":"10.14201/adcaij.27271","DOIUrl":"https://doi.org/10.14201/adcaij.27271","url":null,"abstract":"The Sensor Nodes (SN) play an important role in various hazardous applications environments such as military surveillance, forests, battlefield, etc. The Wireless Sensor Network (WSN) comprised multiple numbers of sensor nodes which are used to perform sensing the physical conditions and subsequently transmitting data to the Base Station (BS). The nodes have limited batteries. The random distribution of nodes in the hazardous areas causes overlapping of nodes and coverage hole issues in the network. The Coverage Optimization and Resolve Hole Healing (CHOP) Protocol is proposed to optimize the network's overlapping and resolve the coverage hole problem. The working phases of the proposed protocol are network initialization, formation of the cluster, Selection of Cluster Head, and sleep and wake-up phase. The issues are optimized, and maximum coverage is achieved for a specific sensing range. Using statistics and probability theory, a link is established between the radius of the node and the coverage area. The protocol used the sleep and wake phase to select optimal nodes active to achieve maximum coverage. The proposed protocol outperformed and showed improvements in the network's performance and lifetime compared to LEACH, TEEN, SEP, and DEEC protocols.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"2 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85140237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Deep Learning Approach to Technician Routing and Scheduling Problem 技术人员路由调度问题的深度学习方法

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-10-21 DOI: 10.14201/adcaij.27393

Engin Pekel

引用次数: 0

Time-Windowed Vehicle Routing Problem: Tabu Search Algorithm Approach 时间窗车辆路径问题:禁忌搜索算法

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-10-21 DOI: 10.14201/adcaij.27533

Hasibe Berfu Demir, Ebru Pekel Özmen, Şakir Esnaf

引用次数: 1

Efficient Content Based Video Retrieval System by Applying AlexNet on Key Frames 基于关键帧AlexNet的高效视频检索系统

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-10-21 DOI: 10.14201/adcaij.27430

Altaf Hussain, Mehtab Ahmad, Tariq Hussain, Ijaz Ullah

{"title":"Efficient Content Based Video Retrieval System by Applying AlexNet on Key Frames","authors":"Altaf Hussain, Mehtab Ahmad, Tariq Hussain, Ijaz Ullah","doi":"10.14201/adcaij.27430","DOIUrl":"https://doi.org/10.14201/adcaij.27430","url":null,"abstract":"The video retrieval system refers to the task of retrieving the most relevant video collection, given a user query. By applying some feature extraction models the contents of the video can be extracted. With the exponential increase in video data in online and offline databases as well as a huge implementation of multiple applications in health, military, social media, and art, the Content-Based Video Retrieval (CBVR) system has emerged. The CBVR system takes the inner contents of the video frame and analyses features of each frame, through which similar videos are retrieved from the database. However, searching and retrieving the same clips from huge video collection is a hard job because of the presence of complex properties of visual data. Video clips have many frames and every frame has multiple properties that have many visual properties like color, shape, and texture. In this research, an efficient content-based video retrieval system using the AlexNet model of Convolutional Neural Network (CNN) on the keyframes system has been proposed. Firstly, select the keyframes from the video. Secondly, the color histogram is then calculated. Then the features of the color histogram are compared and analyzed for CBVR. The proposed system is based on the AlexNet model of CNN and color histogram, and extracted features from the frames are together to store in the feature vector. From MATLAB simulation results, the proposed method has been evaluated on benchmark dataset UCF101 which has 13320 videos from 101 action categories. The experiments of our system give a better performance as compared to the other state-of-the-art techniques. In contrast to the existing work, the proposed video retrieval system has shown a dramatic and outstanding performance by using accuracy and loss as performance evaluation parameters.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"129 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76745818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative Evaluation of Techniques for n-way Stream Joins in Wireless Sensor Networks 无线传感器网络中n向流连接技术的比较评价

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-10-21 DOI: 10.14201/adcaij.27777

Boubekeur Djail

{"title":"Comparative Evaluation of Techniques for n-way Stream Joins in Wireless Sensor Networks","authors":"Boubekeur Djail","doi":"10.14201/adcaij.27777","DOIUrl":"https://doi.org/10.14201/adcaij.27777","url":null,"abstract":"In wireless sensor networks, sensor data are accessed using relational queries. Join queries are commonly used to retrieve the data from multiple tables stored in different parts of a wireless sensor network. However, such queries require large amounts of energy. Many studies have intended to reduce query energy consumption. However, most of the proposed techniques addressed binary joins which are performed between static tables. N-way joins between data streams were rarely considered. Join queries using data streams work continuously and require increasing energy, which is why n-way joins involving several tables consume so much energy. Thus, the challenge lies in reducing energy dissipation. Additionally, it is necessary to determine the appropriate execution order for an n-way join. The number of possible implementations of an n-way join grows exponentially with the tables’ number. In this paper, interesting approaches for n-way joins between streams of data are evaluated. The methods that have been compared are extern-join, Sens-join of Stern et al, and the two techniques NSLJ (N-way Stream Local Join) and NSLSJ (N-way Stream Local Semi-Join). Comparisons are conducted according to several parameters to determine which use case is appropriate for each technique. NSLSJ works best for join queries with low join selectivity factors, while extern-join is more suitable for queries with very high selectivity factors.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"83 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73959957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0